Editorial: Computational modeling of various procedures in thermal therapy of human tumors
Bibliographic record
Abstract
Editorial on the Research Topic Computational modeling of various procedures in thermal therapy of human tumorsCancer is the leading cause in many countries, which lead to almost 10 million deaths in 2020 (Sung et al., 2021).In the meantime, the traditional cancer therapeutics including radiotherapy, chemotherapy, and surgery, all suffer from different side effects or limitations (Ren et al., 2022).Thermal therapy, commonly known as hyperthermia has been used to treat cancer and other diseases since at least 4,000 years ago (Hornback, 1989;Glazer and Curley, 2010).Hyperthermia cancer therapy involves subjecting a tumor (locally or regionally) to a temperature within 42 ° C-48 ° C for a duration of an hour to a few minutes depending on the temperature used.Due to its advantages of minimal-or noninvasive nature, it has attracted the interest of researchers worldwide.Thermal therapy can be characterized as photothermal therapy, ultrasound thermal therapy, radiofrequency thermal therapy, magnetic hyperthermia, etc., according to the heat generation methods (Khurana et al., 2022;Singh et al., 2022).In the context of photothermal therapy, an indirect heating strategy has been proposed in which the tissue around the tumor is heated instead of the tumor itself, which will reduce or even stop the oxygen supply of the tumor cells (Dombrovsky et al., 2012;Dombrovsky, 2022).Due to the development of nanotechnology, plasmonic nanoparticles are employed to enhance energy absorption during photothermal therapy, termed as plasmonic photothermal therapy, which is very popular in recent years.The most important Research Topic for thermal therapy is precise temperature control to kill the tumor cell selectively.Accurate numerical modeling of thermal therapy, especially the heat transfer process, is very important for the optimization and real time adjustment of thermal therapy procedure or pre-treatment planning before thermal therapy (Ren et al., 2022).This Research Topic aims to report the important development in the field of computational modeling of different kinds of procedures involved in thermal therapy, including but not limited to heat transfer, light propagation, blood flow, thermal damage, etc., which is very important to quantify the optimal thermal dosage for patient-specific settings.In this Research Topic, Mariappan et al. has proposed a point source model to predict the power generated by the needle which does not require to obtain the complex electric field during radiofrequency ablation.The proposed model was applied to both two-dimensional
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.027 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".